Indian researchers use ML to analyze CEO performances

Machine Learning techniques will analyze CEOs behavior, style, and communication to analyze the performance of firms.

FREMONT, CA: Effective communication skills lead to convey bright ideas in a better and more straightforward manner. Organizational communication helps the organization to be successful. CEO is considered as a person who plays a central and vital leadership role at any company. A good CEO must have the ability to convey their ideas through excellent communicative skills. CEO might influence firm strategy by communicating his/her ideas to internal and external stakeholders.

Tarun Khanna and Prithwiraj Choudhury, the Jorge Paulo Lemann Professor in the Strategy Unit at HBS; Columbia Business School professor Dan Wang; and doctoral student Natalie Carlson, use machine-learning technology to derive the relation between a CEO's communications style and company performance.

Some years ago, the idea of machine learning would have stoked the worst kind of sci-fi nightmares about robots taking over the planet. These days, machine learning is reigning the industries and helping human in various ways. Now machine learning is operating many aspects of different sectors. Some ML is working behind the stage and is invisible, and others are working on stage and are visible.

The team of researchers followed the following pattern to learn CEOs behaviors:

1. Behavior analysis:

The first challenge in front of researchers was to find enough CEOs to test. After seeing them, they were analyzed using three machine-learning techniques. In the first phase, researchers examined the words used by CEOs by statistical inference to generate 100 topics like marketing, corporate boards, and personal family history. Each CEO was scored based on ?topic entropy,? which means for how much time one can stick to one topic rather than bouncing from subject to subject.

The second technique divided the words as positive and negative. Then observed how much the speaker fluctuated between the positive and negative words.

In the third phase, researchers examined the non-verbal communication of CEOs. Computer-vision applications analyzed their facial expressions like contempt, anger, disgust, happiness, neutral, sadness, fear, and surprise. This test was carried out by computer analysis in a fast and cheap way.

2. Correlated performance with behavior:

According to the results of the test conducted, researchers divided the CEOs into different groups based on their communication style. The group of CEOs who used positive language and a range of facial expressions were deemed Excitable. The CEOs, who showed anger, contempt, and disgust but also a fair amount of neutral expressions were grouped as Stern people. Others with outstanding topic entropy and who had happy and contemptuous facial expression were called Rambling. Those with several ranges of emotions were named as Dramatic, while that showcased sadness and negativity were termed Melancholy.

By analyzing both text and facial features together, researchers were able to conclude by multiple dimensions and styles. After completing, the researcher tried to correlate the communications style and other aspects of the business.

Humans and other animals exhibit natural intelligence (NI), Artificial intelligence (AI) is programmed into machines. Human behavior detection and activity recognition is a very active research area in Artificial Intelligence through computer vision. Nowadays, we are living in a modern era where algorithms lead industries. These algorithms know us better than we know ourselves.

There are various ways through which machine learning detect human behavior:

Human is a bundle of practices, actions, likes, and dislikes. Machine learning with AI can predict human behavior with accuracy. Machine learning and AI support each other on the same platform to perform these tasks. The work of machine learning is to processes the data and to identify trends and patterns, while the AI technology leverages that information to generate recommendations and modifications or to initiate actions. Many behaviors can be predicted with high accuracy as humans tend to follow established habits. This process uses data which is fed into machines.

? By mixing human behavioral psychology with machine learning:

Technologies have progressed in such a manner, that it can now identify human mood based on speech patterns, facial expressions, and physical markers. Mood, physical markers and speech patterns reveal a lot about a person. Speech patterns can disclose an individual's intelligence and education level to their current stress levels. Physical markers can administer medications or adjusting the temperature, and other environmental conditions, the mood could be used to generate other recommendations.

? Continuous learning:

The machine keeps on learning and accepting data to detect human behaviors perfectly. Enough data gives the potential to these detectors machine to perform their job error-free.

As it is noticed that machine learning is into every field, and doing all sorts of works, it is considered as one of the hot topics in the industry. These technologies can remold future sectors in a better manner. There is a whole ocean of data out there that people aren't using," says Tarun Khanna. By utilizing that information, one could get answers for many questions that are essential to business, he added.

The researchers claim that these techniques are relatively simple and easy-to-use. Business analysts could use these machine-learning techniques to analyze communications and human behaviors at the meeting and in other working environments. With the assistance of the ML, now it will be easier to analyze the performance of CEOs.